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Efficient Probabilistic Diagnostics for Electrical Power SystemsWe consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.
Document ID
20110012876
Acquisition Source
Ames Research Center
Document Type
Technical Memorandum (TM)
Authors
Mengshoel, Ole J.
(Universities Space Research Association Moffett Field, CA, United States)
Chavira, Mark
(California Univ. Los Angeles, CA, United States)
Cascio, Keith
(California Univ. Los Angeles, CA, United States)
Poll, Scott
(NASA Ames Research Center Moffett Field, CA, United States)
Darwiche, Adnan
(NASA Ames Research Center Moffett Field, CA, United States)
Uckun, Serdar
(Palo Alto Research Center, Inc. Palo Alto, CA, United States)
Date Acquired
August 25, 2013
Publication Date
November 6, 2008
Subject Category
Statistics And Probability
Report/Patent Number
ARC-E-DAA-TN-210
NASA/TM-2008-214589
Funding Number(s)
CONTRACT_GRANT: NCC2-1426
CONTRACT_GRANT: NNA07BB97C
Distribution Limits
Public
Copyright
Public Use Permitted.
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